ieeexplore最新文献_第4页

Preparation of Nanofibrous Membranes Containing Carbon Dots Composited With TiO2 Photocatalyst and Their Removal Rate of Methylene Blue Under Visible Light TiO2光催化剂复合碳点纳米纤维膜的制备及可见光下对亚甲基蓝的去除率

IF 2.1 4区工程技术

IEEE Transactions on Nanotechnology Pub Date : 2025-07-02 DOI: 10.1109/TNANO.2025.3584828

Yu-Hsun Nien;Yu-Ping Wang

{"title":"Preparation of Nanofibrous Membranes Containing Carbon Dots Composited With TiO2 Photocatalyst and Their Removal Rate of Methylene Blue Under Visible Light","authors":"Yu-Hsun Nien;Yu-Ping Wang","doi":"10.1109/TNANO.2025.3584828","DOIUrl":"https://doi.org/10.1109/TNANO.2025.3584828","url":null,"abstract":"As the industrialization is improving by way of science and technology in society, water pollution has become increasingly serious. Non-degradable organic matter exists in wastewater, which causes environmental deterioration. In order to solve this problem, we select titanium dioxide (TiO2) as the photocatalyst material with high activity, chemical stability and low cost. However, pure TiO2 has a large band gap (3.2 eV) and can only be activated under ultraviolet (UV) light. Therefore, TiO2 has to be modified to fit our requirement. Carbon dots (CDs) have up-conversion and down-conversion photoluminescence and inhibit the recombination of electron-hole pairs, Adding CDs can reduce the band gap width of TiO2, and increase the absorption of visible light significantly, thereby improving photocatalytic efficiency. We use citric acid as the carbon source and urea as the nitrogen source to prepare CDs by using the hydrothermal method, and prepare the CDs/TiO2 composite photocatalyst through the sol-gel method. The CDs/TiO2 composite photocatalyst shows stable and efficient photocatalytic performance for removal of methylene blue (MB), with a removal rate of 95.34%. In order to reuse the CDs/TiO2 composite photocatalyst powder, we use electrospinning technology to combine CDs/TiO2 composite photocatalyst with nylon 6,6 nanofibrous membranes. After three cycle tests, we confirm that it is recyclable and practical, and its removal rate is also increased to 99.39%.","PeriodicalId":449,"journal":{"name":"IEEE Transactions on Nanotechnology","volume":"24 ","pages":"338-346"},"PeriodicalIF":2.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Models for Chlorophyll Content Estimation in Wheat Leaves From Multiangular Reflection Spectra 基于多角度反射光谱估算小麦叶片叶绿素含量的机器学习模型

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583256

Walter O. C. Flores;Felipe Hornung;Katia Christina Zuffellato-Ribas;Marcia Muller;José Luís Fabris;André Eugenio Lazzaretti

{"title":"Machine Learning Models for Chlorophyll Content Estimation in Wheat Leaves From Multiangular Reflection Spectra","authors":"Walter O. C. Flores;Felipe Hornung;Katia Christina Zuffellato-Ribas;Marcia Muller;José Luís Fabris;André Eugenio Lazzaretti","doi":"10.1109/JSEN.2025.3583256","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583256","url":null,"abstract":"Chlorophyll is a crucial pigment in plants that converts sunlight into chemical energy during photosynthesis, whereas nitrogen is an essential nutrient associated with the growth and development of crops. As nitrogen and chlorophyll contents in the plants are closely related, monitoring the chlorophyll content allows for ascertaining the photosynthetic rate and dry matter production, which contributes to the optimization of crop production. In this sense, there is great interest in developing nondestructive and reliable methods for chlorophyll quantification in the field. This work proposes a nondestructive method based on the multiangular reflectance spectroscopy assisted by artificial intelligence (AI) for quantifying chlorophyll in wheat leaves. Deep neural networks (DNNs), minimally random convolutional kernel transform (MiniRocket), extreme gradient boosting regressor (XGBRegressor), random forest (RF) regression, and decision tree (DT) regressor models were trained and tested with reflection spectra. The spectral range from 450 to 750 nm was preprocessed by the Savitzky-Golay filter and principal component analysis (PCA). Chlorophyll content was determined using a fresh mass extraction methodology and absorption spectroscopy. Results indicate the effectiveness of machine learning models in predicting total chlorophyll content in wheat leaves from reflection spectra taken at multiple arbitrarily chosen angles. The DNN model determined total chlorophyll content with a mean absolute error (MAE) of 0.022 mg/g and a root mean squared error (RMSE) of 0.032 mg/g. In addition, we provide a Shapley additive explanations (SHAP) analysis to determine the most relevant spectral ranges for the model prediction and their relation with chlorophylls a and b. The results together form a relevant and original contribution to quantifying and interpreting chlorophyll in wheat leaves using nondestructive approaches.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29252-29261"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Energy Prediction Model for Resource-Constrained IoT Devices With Energy Harvesting 基于能量收集的资源受限物联网设备混合能量预测模型

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583496

Sami Acik;Selahattin Kosunalp

{"title":"A Hybrid Energy Prediction Model for Resource-Constrained IoT Devices With Energy Harvesting","authors":"Sami Acik;Selahattin Kosunalp","doi":"10.1109/JSEN.2025.3583496","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583496","url":null,"abstract":"In energy-harvesting Internet of Things (EH-IoT) environments, edge devices face significant challenges due to limited computational and energy resources. Accurate energy prediction is essential to ensure uninterrupted operation, particularly when relying on intermittent renewable sources like solar energy. This article proposes a novel hybrid energy prediction model (HEP-IoT) designed specifically for resource-constrained IoT devices. HEP-IoT integrates adaptive weighting strategies derived from historical trends, recent slot observations, and the same-slot values from previous days. The model dynamically adjusts its prediction weights based on environmental variability and includes a rapid anomaly detection mechanism to improve responsiveness during sudden energy drops. Unlike conventional fixed-weight models, HEP-IoT achieves a balance between prediction accuracy and computational efficiency. Extensive experiments on three real-world solar energy datasets demonstrate that HEP-IoT outperforms the existing models, such as exponentially weighted moving average (EWMA), weather-conditioned moving average (WCMA), and Pro-Energy in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE), making it a practical and reliable solution for EH-IoT scenarios.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"30074-30085"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CMOS-Integrated Flexible Piezoelectric Sensor Arrays for High-Resolution Spatiotemporal Tactile Sensing 用于高分辨率时空触觉传感的cmos集成柔性压电传感器阵列

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3582903

Po-Jui Ku;Michael S.-C. Lu

引用次数: 0

Investigation of Room-Temperature-Stabilized Disk Resonator Gyroscope 室温稳定盘式谐振陀螺仪的研究

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583297

Zhenjun Wang;Yi Zhou;Lemin Zhang;Fumin Liu;Bo Jiang;Jing Zhang;Tong Zhou;Yan Su

{"title":"Investigation of Room-Temperature-Stabilized Disk Resonator Gyroscope","authors":"Zhenjun Wang;Yi Zhou;Lemin Zhang;Fumin Liu;Bo Jiang;Jing Zhang;Tong Zhou;Yan Su","doi":"10.1109/JSEN.2025.3583297","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583297","url":null,"abstract":"In this work, a room-temperature-stabilized disk resonator gyroscope (RTDRG) using a thermoelectric cooler (TEC) is proposed. Unlike the conventional oven-based temperature control method, the proposed system leverages the bidirectional heating/cooling capability of TECs to maintain the operating temperature of the gyroscope at ambient conditions. This approach significantly reduces start-up time while enhancing the gyroscope’s Q-factor. A prototype RTDRG system was experimentally validated across an extreme temperature range from <inline-formula> <tex-math>$- 40~^{circ }$ </tex-math></inline-formula>C to <inline-formula> <tex-math>$+ 60~^{circ }$ </tex-math></inline-formula>C using a thermal chamber turntable. Experimental results indicate that the RTDRG can be stably controlled at <inline-formula> <tex-math>$25~^{circ }$ </tex-math></inline-formula>C. Comparative analyses reveal substantial performance enhancements under TEC regulation: 90-fold improvement in resonant frequency stability versus temperature, 52-fold increase in Q-factor stability, and 18-fold reduction in temperature-dependent bias drift. Residual drift observed in the system is attributed to unequal thermal resistances from the TEC to the disk resonator gyroscope (DRG) and temperature detector. In future work, the resonant frequency or Q-factor of the DRG will be considered as a temperature-sensitive parameter to develop a higher-precision temperature closed-loop control system.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28038-28045"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NDT Beamforming Algorithm Based on Amplitude Distribution Correlation Factor 基于幅值分布相关因子的无损检测波束形成算法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583495

Peng Wang;Kunlin Wang;Qianwen Li;Lin Tong;Yue Shen;Sirui Chen;Jue Wang;Ping Wang

{"title":"NDT Beamforming Algorithm Based on Amplitude Distribution Correlation Factor","authors":"Peng Wang;Kunlin Wang;Qianwen Li;Lin Tong;Yue Shen;Sirui Chen;Jue Wang;Ping Wang","doi":"10.1109/JSEN.2025.3583495","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583495","url":null,"abstract":"In nondestructive testing ultrasound imaging, B-mode ultrasound line-scan focusing imaging offers the advantages of speed and concentrated energy, but its imaging quality is often compromised by clutter interference. To tackle the problem of clutter interference, this article proposes an adaptive beamforming algorithm based on instantaneous amplitude distribution correlation factor (DCF) for B-mode ultrasound line-scan focusing nondestructive testing imaging. Leveraging the differences in the distribution of instantaneous amplitudes between the background and defective regions, the DCF algorithm identifies the defective part by utilizing the inverse of the product of the range and standard deviation of the echo data. Subsequently, the background is suppressed using the calculated DCF weighting factors, and these factors are further refined by instantaneous sign coherence factor (SCF) to preserve the amplitude of the main lobe. Experimental results indicate that, compared to the delay-and-sum (DAS) algorithm, the DCF algorithm enhances the array performance index (API) and contrast ratio (CR) by 83.08% and 223.85%, respectively, in the 20# gauge steel test block, by 59.82% and 201.15%, respectively, in an aluminum test block, and by 74.52% and 120.68%, respectively, in a weld defect detection block. The DCF algorithm proposed in this article achieves a significant improvement in the quality of ultrasound imaging for nondestructive testing at the cost of a very low increase in algorithm complexity.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29910-29917"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of Static IR-Drop in Large-Scale Event-Based Vision Sensors 静态红外下降对大规模事件视觉传感器的影响

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583394

Silu Cheng;Xuan Qi;Xiaopei Shi;Xinming Ma;Jiangtao Xu

引用次数: 0

Self-Supervised Point Cloud Importance Awareness Network for 2-D LiDAR SLAM 二维激光雷达SLAM自监督点云重要性感知网络

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583431

Wenbo Shi;Haojie Dai;Mazeyu Ji;Yujie Cui;Chengju Liu;Qijun Chen

{"title":"Self-Supervised Point Cloud Importance Awareness Network for 2-D LiDAR SLAM","authors":"Wenbo Shi;Haojie Dai;Mazeyu Ji;Yujie Cui;Chengju Liu;Qijun Chen","doi":"10.1109/JSEN.2025.3583431","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583431","url":null,"abstract":"Data understanding of light detection and ranging (LiDAR) is essential for achieving embodied intelligence in mobile robots. However, the lack of semantic differentiation in 2-D point clouds hinders the potential capabilities of simultaneous localization and mapping (SLAM). To address this challenge, we propose a novel self-supervised learning approach for adaptive point cloud importance awareness. First, we design an effective and lightweight awareness network that assigns importance weights to highlight crucial points based on their inherent features and correlations. Second, we introduce a self-supervised triplet training strategy with multiple objective losses for efficient optimization, eliminating the dependence on manual annotations. Finally, we embed the importance-aware capability into the point cloud registration for SLAM enhancement. Extensive experiments demonstrate that our work achieves rational distribution of weighted point clouds and also yields improvements in both accuracy and robustness across fundamental SLAM components, including scan matching, mapping, and localization. The proposed approach effectively unlocks the latent potential of laser data, leading to superior performance for 2-D LiDAR SLAM systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"30060-30073"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Dual-Electrode PMUT-Based Method to Reduce Ring-Down Time and Improve Both Axial Resolution and Blind Zone 一种基于双电极pmut的减少衰荡时间和提高轴向分辨率和盲区的方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583306

Hantao Guo;Xueying Xiu;Haochen Lyu;Songsong Zhang

引用次数: 0

ISTOF-Net: An ISTA-Based Deep Unfolding Network With Optimized Feature Aggregation Architecture for Image Compressed Sensing ISTOF-Net：一种基于ista的深度展开网络，优化了图像压缩感知的特征聚合结构

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-02 DOI: 10.1109/JSEN.2025.3583291

Shuo Cui;Jingjing Dai;Sisi Zhao;Haifeng Luo

{"title":"ISTOF-Net: An ISTA-Based Deep Unfolding Network With Optimized Feature Aggregation Architecture for Image Compressed Sensing","authors":"Shuo Cui;Jingjing Dai;Sisi Zhao;Haifeng Luo","doi":"10.1109/JSEN.2025.3583291","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583291","url":null,"abstract":"Compressed sensing (CS) has emerged as an efficient framework for sensor-based data acquisition and reconstruction, and is now widely employed in image processing. Deep unfolding networks (DUNs) integrate deep neural networks with optimization algorithms, emerging as a prevalent approach for image CS by enhancing interpretability and reconstruction performance. However, most existing DUNs suffer from insufficient integration and utilization of feature information during the iterative process. To address this limitation and achieve the goal of fusing complementary information to form a richer and finer feature representation, we propose a iterative shrinkage-thresholding algorithm (ISTA)-based DUN with optimized feature aggregation architecture for high-quality image CS, dubbed ISTOF-Net. By introducing a feature aggregation architecture, ISTOF-Net enhances both the integration and utilization of local detail features and global structural features. Specifically, in the reconstruction module, we design a novel optimized feature aggregation module (OFAM), which combines global structural features with local detail features, enhancing their integration and utilization through feature modulation and attention mechanisms. Moreover, we introduce auxiliary convolution module (CM) to further refine and enhance fine image details. In addition, we adopt a lightweight network design, effectively reducing both computational complexity and reconstruction time. Experimental results on datasets acquired by image sensors and on remote sensing image demonstrate that ISTOF-Net outperforms state-of-the-art methods in both reconstruction quality and efficiency. Its high performance and lightweight design make ISTOF-Net ideal for sensor-based imaging systems that demand data compression and rapid imaging.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29271-29283"},"PeriodicalIF":4.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0